Object detection for the impatient PyTorch user

What it does

Detecto is a high-level Python wrapper around PyTorch’s torchvision object-detection models. It handles dataset ingestion from XML or CSV label files, applies transforms, and exposes a Model class for transfer learning on custom classes. Inference and visualization utilities for still images, video files, and live webcam feeds are included.

The interesting bit

The library treats PyTorch’s Faster R-CNN and MobileNet backbones as implementation details, abstracting the scaffolding until you need it. When you do, you can call get_internal_model() to reach through the abstraction and touch the raw torchvision model underneath.

Key highlights

• Ships with pre-trained Faster R-CNN ResNet-50 FPN and optional MobileNet backbones from PyTorch’s model zoo.
• Reads label data from XML or CSV and handles the transform pipeline internally.
• Provides visualization helpers for plotting bounding boxes, prediction grids, and video streams.
• Supports saving and loading model weights, and exposes the underlying PyTorch model for custom tweaks.

Caveats

• Some unit tests fail unless you manually download a separate pre-trained weights file and place it in the test directory.
• The README’s “five lines of code” claim elides the work of preparing and labeling a custom dataset.

Verdict

Worth a look if you want to prototype custom object detection quickly without drowning in PyTorch boilerplate. Seasoned CV engineers who prefer full control over their DataLoaders and anchor boxes will likely find the abstraction too snug.